ABSTRACT
Reduced participation in COVID-19 vaccination programs is a key societal concern. Understanding factors associated with vaccination uptake can help in planning effective immunization programs. We considered 2,890 health, socioeconomic, familial, and demographic factors measured on the entire Finnish population aged 30 to 80 (N=3,192,505) and genome-wide information for a subset of 273,765 individuals. Risk factors were further classified into 12 thematic categories and a machine learning model was trained for each category. The main outcome was uptaking the first COVID-19 vaccination dose by 31.10.2021, which has occurred for 90.3% of the individuals. The strongest predictor category was labor income in 2019 (AUC evaluated in a separate test set = 0.710, 95% CI: 0.708-0.712), while drug purchase history, including 376 drug classes, achieved a similar prediction performance (AUC = 0.706, 95% CI: 0.704-0.708). Higher relative risks of being unvaccinated were observed for some mental health diagnoses (e.g. dissocial personality disorder, OR=1.26, 95% CI : 1.24-1.27) and when considering vaccination status of first-degree relatives (OR=1.31, 95% CI:1.31-1.32 for unvaccinated mothers) We derived a prediction model for vaccination uptake by combining all the predictors and achieved good discrimination (AUC = 0.801, 95% CI: 0.799-0.803). The 1% of individuals with the highest risk of not vaccinating according to the model predictions had an average observed vaccination rate of only 18.8%. We identified 8 genetic loci associated with vaccination uptake and derived a polygenic score, which was a weak predictor of vaccination status in an independent subset (AUC=0.612, 95% CI: 0.601-0.623). Genetic effects were replicated in an additional 145,615 individuals from Estonia (genetic correlation=0.80, 95% CI: 0.66-0.95) and, similarly to data from Finland, correlated with mental health and propensity to participate in scientific studies. Individuals at higher genetic risk for severe COVID-19 were less likely to get vaccinated (OR=1.03, 95% CI: 1.02-1.05). Our results, while highlighting the importance of harmonized nationwide information, not limited to health, suggest that individuals at higher risk of suffering the worst consequences of COVID-19 are also those less likely to uptake COVID-19 vaccination. The results can support evidence-informed actions for COVID-19 and other areas of national immunization programs.
ABSTRACT
BACKGROUNDThe aim of this multinational study was to assess the development of adverse mental health symptoms among individuals diagnosed with COVID-19 in the general population by acute infection severity up to 16 months after diagnosis. METHODSParticipants consisted of 247 249 individuals from seven cohorts across six countries (Denmark, Estonia, Iceland, Norway, Scotland, and Sweden) recruited from April 2020 through August 2021. We used multivariable Poisson regression to contrast symptom-prevalence of depression, anxiety, COVID-19 related distress, and poor sleep quality among individuals with and without a diagnosis of COVID-19 at entry to respective cohorts by time (0-16 months) from diagnosis. We also applied generalised estimating equations (GEE) analysis to test differences in repeated measures of mental health symptoms before and after COVID-19 diagnosis among individuals ever diagnosed with COVID-19 over time. FINDINGSA total of 9979 individuals (4%) were diagnosed with COVID-19 during the study period and presented overall with a higher symptom burden of depression (prevalence ratio [PR] 1{middle dot}18, 95% confidence interval [95% CI] 1{middle dot}03-1{middle dot}36) and poorer sleep quality (1{middle dot}13, 1{middle dot}03-1{middle dot}24) but not with higher levels of symptoms of anxiety or COVID-19 related distress compared with individuals without a COVID-19 diagnosis. While the prevalence of depression and COVID-19 related distress attenuated with time, the trajectories varied significantly by COVID-19 acute infection severity. Individuals diagnosed with COVID-19 but never bedridden due to their illness were consistently at lower risks of depression and anxiety (PR 0{middle dot}83, 95% CI 0{middle dot}75-0{middle dot}91 and 0{middle dot}77, 0{middle dot}63-0{middle dot}94, respectively), while patients bedridden for more than 7 days were persistently at higher risks of symptoms of depression and anxiety (PR 1{middle dot}61, 95% CI 1{middle dot}27-2{middle dot}05 and 1{middle dot}43, 1{middle dot}26-1{middle dot}63, respectively) throughout the 16-month study period. CONCLUSIONAcute infection severity is a key determinant of long-term mental morbidity among COVID-19 patients.
ABSTRACT
BackgroundDecisions about the continued need for control measures and the effect of introducing COVID-19 vaccinations rely on accurate and population-based data on SARS-CoV-2 positivity and risk factors for testing positive. MethodsIn this interrupted time series of population-based nationwide cross-sectional studies, data from nasopharyngeal testing and questionnaires were used to estimate the SARS-CoV-2 RNA prevalence and factors associated with test positivity over the 1st year of the COVID-19 epidemic. The study is registered with the ISRCTN Registry, ISRCTN10182320. ResultsBetween April 23, 2020 and February 2, 2021, results were available from 34,915 individuals and 27,870 samples from 11 consecutive studies. The percentage of people testing positive for SARS-CoV-2 decreased from 0.27% (95% CI 0.10% - 0.59%) in April to 0.04% (95% CI 0.00% - 0.22%) by the end of May and remained very low (0.01%, 95% CI 0.00% - 0.17%) until the end of August, followed by an increase since November (0.37%, 95% CI 0.18% - 0.68%) that escalated to 2.69% (95% CI 2.08% - 2.69%) in January 2021. In addition to substantial change in time, an increasing number of household members (for one additional OR 1.15, 95% CI 1.02-1.29), reporting current symptoms of COVID-19 (OR 2.21, 95% CI 1.59-3.09), and completing questionnaire in the Russian language (OR 1.85, 95% CI 1.15-2.99) were associated with increased odds for SARS-CoV-2 RNA positivity. ConclusionsSARS-CoV-2 population prevalence needs to be carefully monitored as vaccine programmes are rolled out in order to inform containment decisions. Strengths and limitations of this studyO_LIOur study relies upon nation-wide and population-based data on SARS-CoV-2 prevalence, and presents changes in prevalence over the whole 1st year of the Covid-19 epidemic. C_LIO_LIOur analysis of SARS-CoV-2 infection risk factors is not limited to notification or health care-based case data. C_LIO_LISelection bias may have been introduced as a result of low response rate. The direction of bias is unclear, but most likely operates rather uniformly over the period of observation, though this presents less of a threat to the SARS-CoV-2 prevalence trend analysis. C_LIO_LIOur data could be used to adequately project the future course of the SARS-CoV-2 epidemic and the effect of control measures. C_LI
